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Llmops Jobs in Missouri (NOW HIRING)

Jobgether

$100K - $120K/yr

You will combine software engineering, machine learning, and MLOps/LLMOps expertise to create scalable and reliable AI products. The position involves solving complex business problems through ...

ITTConnect

Senior AI Engineer (Java)

Saint Louis, MO · On-site

$117K - $154K/yr

Contribute to LLMOps and MLOps practices, including evaluation, monitoring, versioning, deployment, and lifecycle management. * Collaborate with AI/ML, product, platform, data, and DevOps teams.

ITTConnect

Senior AI Engineer (Java)

Saint Louis, MO · On-site

$121K - $159K/yr

Contribute to LLMOps and MLOps practices, including evaluation, monitoring, versioning, deployment, and lifecycle management. Collaborate with AI/ML, product, platform, data, and DevOps teams.

MasterCard

Senior AI Engineer

O Fallon, MO · Hybrid

$97K - $134K/yr

Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure ...

Mastercard, Inc.

Senior AI Engineer

O Fallon, MO · On-site

$97K - $134K/yr

As a technical leader, you will set the engineering standard for Gen AI development - driving innovation across agentic orchestration, retrieval-augmented generation, LLMOps, and responsible AI ...

Kforce Technology Staffing

SRE (AI/ML)

Maryland Heights, MO · On-site

$55 - $73.25/hr

The role blends classic SRE and cloud administration with modern MLOps and LLMOps practices. REQUIREMENTS: * 8+ years total experience with at least 3+ years as SRE/DevOps/Cloud Admin on AWS in ...

Walmart

(USA) Staff, Software Engineer

Noel, MO · On-site

$110K - $220K/yr

Understanding of responsible AI practices, model evaluation, guardrails, or LLMOps. At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a ...

Imperial PFS

Senior Security AI Engineer

Kansas City, MO · Hybrid

$111K - $153K/yr

Securing MLOps/LLMOps pipelines and implementing guardrails and monitoring. * Leading AI red teaming engagements and integrating AI telemetry into security operations. * Ensuring compliance with ...

Imperial PFS

Senior Security AI Engineer

Kansas City, MO · On-site

$125 - $150/hr

Securing MLOps/LLMOps pipelines and implementing guardrails and monitoring. * Leading AI red‑teaming engagements and integrating AI telemetry into security operations. * Ensuring compliance with ...

Walmart

(USA) Staff, Software Engineer

Cassville, MO · On-site

$110K - $220K/yr

Understanding of responsible AI practices, model evaluation, guardrails, or LLMOps. At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a ...

Accenture

AI & HPC Infrastructure Engineer

Saint Louis, MO · On-site

$97K - $127K/yr

Preferred Skills and Qualifications: * 2+ years of experience implementing MLOps, LLMOps, agentic AI, and DevSecOps frameworks to enable secure, automated, governed, and reproducible AI workflows ...

Walmart

(USA) Staff, Software Engineer

Anderson, MO · On-site

$110K - $220K/yr

Understanding of responsible AI practices, model evaluation, guardrails, or LLMOps. At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a ...

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All JobsLlmops JobsLlmops Jobs in Missouri

Llmops information

What is the difference between Llmops vs Data Scientist?

AspectLlmopsData Scientist
Required credentialsKnowledge of machine learning, AI frameworks, cloud platformsStatistics, programming, data analysis skills
Work environmentAI/ML teams, cloud environments, deployment pipelinesData analysis, modeling, reporting in various industries
Employer usageTech companies, AI startups, research labsFinance, healthcare, tech, retail

While both roles involve working with data and machine learning, Llmops focuses on deploying and maintaining large language models in production environments, requiring expertise in AI infrastructure. Data Scientists primarily analyze data, build models, and generate insights. Llmops professionals ensure models operate efficiently at scale, whereas Data Scientists develop the models and interpret results.

What are popular job titles related to Llmops jobs in Missouri?

For Llmops jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Llmops jobs in Missouri look for?

The top searched job categories for Llmops jobs in Missouri are:

What cities in Missouri are hiring for Llmops jobs?

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Llmops jobs near you
Infographic showing various Llmops job openings in Missouri as of August 2026, with employment types broken down into 60% Full Time, and 40% Contract. Highlights an 80% In-person, and 20% Remote job distribution.

AI/ML & Forward Deployed Engineer

On-site, Remote

Jobgether
See more jobs at Jobgetherexternal

$100K - $120K/yr

Full-time

Posted 13 days ago

Key responsibilities

  • Design, develop, and deploy machine learning, AI, and GenAI solutions from proof of concept through production.

  • Build scalable AI services and integrations using APIs and event-driven architectures, and support solutions through production operations.

  • Establish and maintain MLOps and LLMOps practices covering deployment, automation, monitoring, and lifecycle management.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML & Forward Deployed Engineer based in Netherlands.

This role offers the opportunity to build and deploy high-impact AI, machine learning, and generative AI solutions from initial concept through production.
You will combine software engineering, machine learning, and MLOps/LLMOps expertise to create scalable and reliable AI products.
The position involves solving complex business problems through technologies such as LLMs, RAG pipelines, NLP, forecasting, and anomaly detection.
You will work across the full AI lifecycle, from experimentation and evaluation to deployment, monitoring, and continuous improvement.
Strong attention to security, governance, observability, and data quality will be essential when delivering production-ready solutions.
You will also collaborate closely with stakeholders to translate business needs into practical technical solutions and measurable outcomes.
This is an excellent opportunity for an experienced engineer who enjoys combining advanced AI technologies with hands-on implementation and real-world impact.

Accountabilities
  • Design, develop, and deploy machine learning, AI, and GenAI solutions from proof of concept through production.
  • Build and optimize ML models and applications covering deep learning, NLP, forecasting, classification, regression, and anomaly detection use cases.
  • Develop production-grade GenAI applications using LLMs, RAG pipelines, embeddings, retrieval optimization, reranking, and prompt engineering.
  • Design and implement AI evaluation frameworks to assess model quality, reliability, relevance, and performance.
  • Build scalable AI services and integrations using REST and gRPC APIs as well as event-driven architectures.
  • Establish and maintain MLOps and LLMOps practices covering deployment, automation, versioning, monitoring, and lifecycle management.
  • Containerize and orchestrate AI applications using Docker and Kubernetes and integrate them into robust CI/CD pipelines.
  • Implement model monitoring, drift detection, performance tracking, and processes for continuous model improvement.
  • Ensure AI solutions meet enterprise requirements for data quality, governance, security, role-based access control, encryption, and auditability.
  • Collaborate with business and technical stakeholders to understand requirements, identify opportunities for AI adoption, and translate them into effective technical solutions.
  • Support solutions through production operations, troubleshooting, optimization, and ongoing improvements.
  • Apply strong engineering practices to ensure AI systems are scalable, secure, observable, maintainable, and aligned with business objectives.
Requirements
  • 8+ years of professional software engineering or technical engineering experience.
  • Strong hands-on experience in Machine Learning and AI/ML Engineering.
  • Advanced Python development skills and practical experience with deep learning and machine learning techniques.
  • Experience with NLP, forecasting, classification, regression, and anomaly detection.
  • Proven experience building GenAI applications using LLMs and Retrieval-Augmented Generation (RAG) architectures.
  • Strong understanding of embeddings, retrieval tuning, reranking, prompt engineering, and AI/LLM evaluation methodologies.
  • Solid knowledge of MLOps and LLMOps principles and practices across the AI development lifecycle.
  • Hands-on experience with Docker, Kubernetes, and CI/CD technologies for production deployments.
  • Experience designing and developing REST and gRPC APIs and event-driven services.
  • Knowledge of model monitoring, model versioning, drift detection, performance evaluation, and model lifecycle management.
  • Strong understanding of data quality, data governance, security controls, RBAC, encryption, and audit trails.
  • Ability to work effectively with both technical and non-technical stakeholders and translate business challenges into practical AI solutions.
  • Strong problem-solving, analytical, communication, and collaboration skills.
  • Ability to operate effectively in fast-moving environments while balancing experimentation with production reliability.
Benefits
  • Competitive annual salary of $100,000-$120,000.
  • Full-time opportunity with a remote working model.
  • Opportunity to work on cutting-edge AI, machine learning, and GenAI solutions.
  • Hands-on exposure to LLMs, RAG, MLOps, LLMOps, Kubernetes, and cloud-native engineering practices.
  • Opportunity to contribute to AI solutions from concept and experimentation through production deployment.
  • Work on technically challenging projects with a strong focus on scalability, security, governance, and observability.
  • Collaborative environment with opportunities to engage directly with stakeholders and influence AI solution strategy.
  • Opportunity to expand expertise across modern AI engineering, machine learning, and production software development.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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